论文标题

多尺度流传输的随机动力学方案,具有不确定性定量

A stochastic kinetic scheme for multi-scale flow transport with uncertainty quantification

论文作者

Xiao, Tianbai, Frank, Martin

论文摘要

气流在不同的特征量表上显示出各种各样的行为。鉴于流体理论中的粗粒化建模,流场溶液与真实物理学之间可能存在相当大的不确定性。为了研究从分子到流体动力学水平的不确定性的出现,传播和演变,带来了巨大的机会和挑战,以发展合理的理论和可靠的多尺度数值算法。在本文中,将开发出一种新的随机动力学方案,其中包括通过随机盖尔金和搭配方法杂交的不确定性。基于Boltzmann-BGK模型方程,该方案中采用了与比例有关的解决方案,以在离散的时间空间域中构建管理方程。因此,可以以自适应方式相对于不同的物理特征量表和数值分辨率来恢复典型的流动物理。我们证明,该方案在不同的流动方案中正式渐近地保护了随机变量,因此可以在不确定性的效果下用于研究多规模非平衡气体动力学。 显示了几个数值实验来验证该方案。我们进行了新的物理观察,例如从连续体到稀有政权的不确定性的波浪传播模式。这些现象将被定量提出和分析。当前的方法提供了一种新颖的工具,可以量化多尺度流动进化中的不确定性。

Gaseous flows show a diverse set of behaviors on different characteristic scales. Given the coarse-grained modeling in theories of fluids, considerable uncertainties may exist between the flow-field solutions and the real physics. To study the emergence, propagation and evolution of uncertainties from molecular to hydrodynamic level poses great opportunities and challenges to develop both sound theories and reliable multi-scale numerical algorithms. In this paper, a new stochastic kinetic scheme will be developed that includes uncertainties via a hybridization of stochastic Galerkin and collocation methods. Based on the Boltzmann-BGK model equation, a scale-dependent evolving solution is employed in the scheme to construct governing equations in the discretized temporal-spatial domain. Therefore typical flow physics can be recovered with respect to different physical characteristic scales and numerical resolutions in a self-adaptive manner. We prove that the scheme is formally asymptotic-preserving in different flow regimes with the inclusion of random variables, so that it can be used for the study of multi-scale non-equilibrium gas dynamics under the effect of uncertainties. Several numerical experiments are shown to validate the scheme. We make new physical observations, such as the wave-propagation patterns of uncertainties from continuum to rarefied regimes. These phenomena will be presented and analyzed quantitatively. The current method provides a novel tool to quantify the uncertainties within multi-scale flow evolutions.

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